Methods and apparatus for mapping source location for input data to a graphics processing unit

ABSTRACT

The present disclosure relates to methods and apparatus for mapping a source location of input data for processing by a graphics processing unit. The apparatus can configure a processing element of the graphics processing unit with a predefined rule for decoding a data source parameter for executing a task by the graphics processing unit. Moreover, the apparatus can store the parameter in local storage of the processing element and configure the processing element to decode the parameter according to the at least one predefined rule to determine a source location of the input data and at least one relationship between invocations of the task. The apparatus can also load, to the local storage of the processing element, the input data from a plurality of memory addresses of the source location determined by the parameter. A one logic unit can then execute the task on the loaded input data.

TECHNICAL FIELD

The present disclosure relates generally to processing systems and, moreparticularly, to one or more techniques for mapping source location datato a graphics processing unit for improved task performance.

INTRODUCTION

Computing devices often utilize a graphics processing unit (GPU) toaccelerate the rendering of graphical data for display. Such computingdevices may include, for example, computer workstations, mobile phonessuch as so-called smartphones, embedded systems, personal computers,tablet computers, and video game consoles. GPUs execute a graphicsprocessing pipeline that includes one or more processing stages thatoperate together to execute graphics processing commands and output aframe. A central processing unit (CPU) may control the operation of theGPU by issuing one or more graphics processing commands to the GPU.Modern day CPUs are typically capable of concurrently executing multipleapplications, each of which may need to utilize the GPU duringexecution. A device that provides content for visual presentation on adisplay generally includes a GPU.

Typically, a GPU is configured to perform the processes in a graphicsprocessing pipeline. For example, programmable shader units may beprovided in a GPU within a graphics processing pipeline for performingspecialized functions of computer graphics special effects. Moreover,GPUs gain performance efficiencies for executing such processes by usingparallel hardware to run tasks for these processes in parallel. Incurrent implementations, a shader unit will allocate its local storageto load and store all source location data of the workload for theoperation to be executed (e.g., the special effect). By doing so, theshader unit consumes significant memory to load and store this sourcelocation data and thus reduces overall performance and power of the GPU.Therefore, there has developed an increased need for improvedutilization of system resources to perform parallel operations by a GPU.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key elements of all aspects nor delineate the scopeof any or all aspects. Its sole purpose is to present some concepts ofone or more aspects in a simplified form as a prelude to the moredetailed description that is presented later.

In an aspect of the disclosure, a method, a computer-readable medium,and an apparatus are provided. The apparatus may be a GPU that includesa graphics processing pipeline. The apparatus may configure a processingelement of the graphics processing unit with at least one predefinedrule for decoding a data source parameter for a task to be executed bythe graphics processing unit; store the data source parameter in localstorage of the processing element; configure the processing element todecode the data source parameter according to the at least onepredefined rule to determine a source location of the input data and atleast one relationship between invocations of the task; load, to thelocal storage of the processing element, the input data from a pluralityof memory addresses of the source location that are determined by thedecoded data source parameter; and configure at least one logic unit ofthe processing element to execute the task on the loaded input data togenerate output data. Moreover, the processor can further configured toconfigure the at least one logic unit to generate the output data basedon a matrix multiply operation. The input data may comprise at least oneinput matrix and the data source parameter comprises a single elementvalue within the at least one input matrix. The at least one processorcan further configured to configure the at least one logic unit todecode the single element value to determine the source location of theplurality of memory addresses. Ina addition, each data input value ofthe at least one matrix is stored in a respective memory address of theplurality of memory addresses and the at least one processor may befurther configured to control the at least one logic unit to calculatethe output data by multiplying the at least one input matrix by one ormore additional input matrices to generate the output data. The at leastone processor can further be configured to store the output data in thelocal storage of the processing element. In addition, the at least oneprocessor may further be configured to define a mode of operation forthe at least one logic unit to execute the task. In an aspect, the taskmay be a fast Fourier transform operation of the loaded input data togenerate the output data and the at least one processor may beconfigured to configure the at least one logic unit to determine shuffleinstructions for the fast Fourier transform operation based on thedefined mode of operation. Moreover, the operation executed by the atleast one logic unit is a deep learning operation and the input data isimage data and the output data for the executed deep learning operationindicates at least one of an identification of content of the imagedata, a prediction of a next image in an image sequence of the contentincluding the image data, and an identification of a missing part of theimage data. In addition, the at least one processor may be configured toload the input data to the local storage of the processing element fromthe electronic memory that is located external to the graphicsprocessing unit. Moreover, the processing element may be a shader unitof the graphics processing unit.

The details of one or more examples of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the disclosure will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram that illustrates an example content processingsystem configured to implement one or more techniques of thisdisclosure.

FIG. 2 is a block diagram that illustrates an example graphicsprocessing unit in accordance with one or more techniques of thisdisclosure.

FIG. 3 is a block diagram that illustrates an example shader unit inaccordance with one or more techniques of this disclosure.

FIG. 4 illustrates an example diagram in accordance with one or moretechniques of this disclosure.

FIG. 5 illustrates an example diagram in accordance with one or moretechniques of this disclosure.

FIG. 6 illustrates an example flowchart of a method in accordance withone or more techniques of this disclosure.

DETAILED DESCRIPTION

Generally, a graphics processing unit (GPU) is a specialized electroniccircuit designed to rapidly manipulate and alter memory to acceleratethe creation of images in a frame buffer intended for output to adisplay device. GPUs can be in embedded systems, mobile phones, personalcomputers, workstations, and the like, as described in detail below withrespect to FIG. 1 . The highly parallel structure of GPUs makes themmore efficient than general-purpose central processing units (CPUs) toprocess large blocks of data in parallel. GPUs include one or moreprogrammable shading units 40 that may concurrently execute multipleinstances of what is commonly referred to as a “shader program.” Theseshader programs may be referred to as “fibers” or “threads” (both ofwhich may refer to a stream of instructions that form a program orthread of execution).

In most compute works, such as image processing or deep learningalgorithms and architectures (e.g., matrix multiply operations), eachtask invocation for tasks running in parallel can be derived by a singleseed input. However, shader units in a GPU perform such operations byallocating its local storage to hold all fiber source location data foreach subgroup, i.e., parallel task of the operation. Effectively, asignificant amount of source input data is loaded into the shader units,which in turn reduces the overall performance and power of the GPU.Aspects of the present disclosure provide apparatuses and methods formapping source location data to a graphics processing unit for improvedtask performance. More particularly, the present disclosure provides fortechniques of configuring one or more logic units of the GPU to decode asingle data location parameter to determine the source location of theinput data and relationships for invocations of the subgroups ofoperation. Moreover, the one or more logic units is configured fordecoding the data location parameter to load the input data from aplurality of memory addresses of electronic memory that are identifiedby the decoded data location identifier. The one or more logic unitsthen executes an operation on the loaded input data to generate outputdata for the designated operation (e.g., image processing or deeplearning operation).

Various aspects of systems, apparatuses, computer program products, andmethods are described more fully hereinafter with reference to theaccompanying drawings. This disclosure may, however, be embodied in manydifferent forms and should not be construed as limited to any specificstructure or function presented throughout this disclosure. Rather,these aspects are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of this disclosure to thoseskilled in the art. Based on the teachings herein, one skilled in theart should appreciate that the scope of this disclosure is intended tocover any aspect of the systems, apparatuses, computer program products,and methods disclosed herein, whether implemented independently of, orcombined with, other aspects of the disclosure. For example, anapparatus may be implemented or a method may be practiced using anynumber of the aspects set forth herein. In addition, the scope of thedisclosure is intended to cover such an apparatus or method which ispracticed using other structure, functionality, or structure andfunctionality in addition to or other than the various aspects of thedisclosure set forth herein. Any aspect disclosed herein may be embodiedby one or more elements of a claim.

Although various aspects are described herein, many variations andpermutations of these aspects fall within the scope of this disclosure.Although some potential benefits and advantages of aspects of thisdisclosure are mentioned, the scope of this disclosure is not intendedto be limited to particular benefits, uses, or objectives. Rather,aspects of this disclosure are intended to be broadly applicable todifferent technologies, system configurations, networks, and processingprotocols, some of which are illustrated by way of example in thefigures and in the following description. The detailed description anddrawings are merely illustrative of this disclosure rather thanlimiting, the scope of this disclosure being defined by the appendedclaims and equivalents thereof.

Several aspects are presented with reference to various apparatus andmethods. These apparatus and methods are described in the followingdetailed description and illustrated in the accompanying drawings byvarious blocks, components, circuits, processes, algorithms, and thelike (collectively referred to as “elements”). These elements may beimplemented using electronic hardware, computer software, or anycombination thereof. Whether such elements are implemented as hardwareor software depends upon the particular application and designconstraints imposed on the overall system.

By way of example, an element, or any portion of an element, or anycombination of elements may be implemented as a “processing system” thatincludes one or more processors (which may also be referred to asprocessing units). Examples of processors include microprocessors,microcontrollers, graphics processing units (GPUs), general purpose GPUs(GPGPUs), central processing units (CPUs), application processors,digital signal processors (DSPs), reduced instruction set computing(RISC) processors, systems-on-chip (SOC), baseband processors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic devices (PLDs), state machines,gated logic, discrete hardware circuits, and other suitable hardwareconfigured to perform the various functionality described throughoutthis disclosure. One or more processors in the processing system mayexecute software. Software can be construed broadly to meaninstructions, instruction sets, code, code segments, program code,programs, subprograms, software components, applications, softwareapplications, software packages, routines, subroutines, objects,executables, threads of execution, procedures, functions, and the like,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise. The term application mayrefer to software. As described herein, one or more techniques may referto an application, i.e., software, being configured to perform one ormore functions. In such examples, the application may be stored on amemory, e.g., on-chip memory of a processor, system memory, or any othermemory. Hardware described herein, such as a processor may be configuredto execute the application. For example, the application may bedescribed as including code that, when executed by the hardware, causesthe hardware to perform one or more techniques described herein. As anexample, the hardware may access the code from a memory and execute thecode accessed from the memory to perform one or more techniquesdescribed herein. In some examples, components are identified in thisdisclosure. In such examples, the components may be hardware, software,or a combination thereof. The components may be separate components orsub-components of a single component.

Accordingly, in one or more examples described herein, the functionsdescribed may be implemented in hardware, software, or any combinationthereof. If implemented in software, the functions may be stored on orencoded as one or more instructions or code on a computer-readablemedium. Computer-readable media includes computer storage media. Storagemedia may be any available media that can be accessed by a computer. Byway of example, and not limitation, such computer-readable media cancomprise a random access memory (RAM), a read-only memory (ROM), anelectrically erasable programmable ROM (EEPROM), optical disk storage,magnetic disk storage, other magnetic storage devices, combinations ofthe aforementioned types of computer-readable media, or any other mediumthat can be used to store computer executable code in the form ofinstructions or data structures that can be accessed by a computer.

In general, this disclosure describes techniques for having a graphicsprocessing pipeline in a single device or multiple devices, analyzinggraphical content, and/or reducing the load of a processing unit, i.e.,any processing unit configured to perform one or more techniquesdescribed herein, including a GPU. For example, this disclosuredescribes techniques for graphics processing in any device that utilizesgraphics processing. Other example benefits are described throughoutthis disclosure.

As used herein, instances of the term “content” may refer to “graphicalcontent,” “image,” and vice versa. This is true regardless of whetherthe terms are being used as an adjective, noun, or other parts ofspeech. In some examples, as used herein, the term “graphical content”may refer to a content processed by one or more processes of a graphicsprocessing pipeline.

In some examples, as used herein, the term “display content” may referto content generated by a processing unit configured to performdisplaying processing. In some examples, as used herein, the term“display content” may refer to content generated by a display processingunit. Graphical content may be processed to become display content. Forexample, a graphics processing unit may output graphical content, suchas a frame, to a buffer (which may be referred to as a framebuffer). Adisplay processing unit may read the graphical content, such as one ormore frames from the buffer, and perform one or more display processingtechniques thereon to generate display content. For example, a displayprocessing unit may be configured to perform composition on one or morerendered layers to generate a frame. As another example, a displayprocessing unit may be configured to compose, blend, or otherwisecombine two or more layers together into a single frame. A displayprocessing unit may be configured to perform scaling, e.g., upscaling ordownscaling, on a frame. In some examples, a frame may refer to a layer.In other examples, a frame may refer to two or more layers that havealready been blended together to form the frame, i.e., the frameincludes two or more layers, and the frame that includes two or morelayers may subsequently be blended.

FIG. 1 is a block diagram that illustrates an example content processingsystem 100 configured to implement one or more techniques of thisdisclosure. The content generation system 100 includes a device 104. Thedevice 104 may include one or more components or circuits for performingvarious functions described herein. In some examples, one or morecomponents of the device 104 may be components of an SOC. The device 104may include one or more components configured to perform one or moretechniques of this disclosure. In the example shown, the device 104 mayinclude a processing unit 120, a content encoder/decoder 122, and asystem memory 124. In some aspects, the device 104 can include a numberof optional components, e.g., a communication interface 126, atransceiver 132, a receiver 128, a transmitter 130, a display processor127, and one or more displays 131. Reference to the display 131 mayrefer to the one or more displays 131. For example, the display 131 mayinclude a single display or multiple displays. In further examples, theresults of the graphics processing may not be displayed on the device,e.g., the first and second display may not receive any frames forpresentment thereon. Instead, the frames or graphics processing resultsmay be transferred to another device.

According to an exemplary aspect, the processing unit 120 may include aninternal memory 121. Moreover, in the exemplary aspect, the processingunit 120 is configured to perform graphics processing, i.e., in graphicsprocessing pipeline 107 as will be discussed in more detail below. Thecontent encoder/decoder 122 may include an internal memory 123. In someexamples, the device 104 may include a display processor, such as thedisplay processor 127, to perform one or more display processingtechniques on one or more frames generated by the processing unit 120before presentment by the one or more displays 131. The displayprocessor 127 may be configured to perform display processing. Forexample, the display processor 127 may be configured to perform one ormore display processing techniques on one or more frames generated bythe processing unit 120. The one or more displays 131 may be configuredto display or otherwise present frames processed by the displayprocessor 127. In some examples, the one or more displays 131 mayinclude one or more of a liquid crystal display (LCD), a plasma display,an organic light emitting diode (OLED) display, a projection displaydevice, an augmented reality display device, a virtual reality displaydevice, a head-mounted display, or any other type of display device.

Memory external to the processing unit 120 and the contentencoder/decoder 122, such as system memory 124, may be accessible to theprocessing unit 120 and the content encoder/decoder 122. For example,the processing unit 120 and the content encoder/decoder 122 may beconfigured to read from and/or write to external memory, such as thesystem memory 124. The processing unit 120 and the contentencoder/decoder 122 may be communicatively coupled to the system memory124 over a bus. In some examples, the processing unit 120 and thecontent encoder/decoder 122 may be communicatively coupled to each otherover the bus or a different connection.

The content encoder/decoder 122 may be configured to receive graphicalcontent from any source, such as the system memory 124 and/or thecommunication interface 126. The system memory 124 may be configured tostore received encoded or decoded graphical content. The contentencoder/decoder 122 may be configured to receive encoded or decodedgraphical content, e.g., from the system memory 124 and/or thecommunication interface 126, in the form of encoded pixel data. Thecontent encoder/decoder 122 may be configured to encode or decode anygraphical content.

The internal memory 121 or the system memory 124 may include one or morevolatile or non-volatile memories or storage devices. In some examples,internal memory 121 or the system memory 124 may include RAM, SRAM,DRAM, erasable programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), flash memory, a magnetic data media or anoptical storage media, or any other type of memory.

In an exemplary aspect, the internal memory 121 or the system memory 124may be a non-transitory storage medium according to some examples. Theterm “non-transitory” may indicate that the storage medium is notembodied in a carrier wave or a propagated signal. However, the term“non-transitory” should not be interpreted to mean that internal memory121 or the system memory 124 is non-movable or that its contents arestatic. As one example, the system memory 124 may be removed from thedevice 104 and moved to another device. As another example, the systemmemory 124 may not be removable from the device 104.

The processing unit 120 may be a central processing unit (CPU), agraphics processing unit (GPU), a general purpose GPU (GPGPU), or anyother processing unit that may be configured to perform graphicsprocessing. In some examples, the processing unit 120 may be integratedinto a motherboard of the device 104. In some examples, the processingunit 120 may be present on a graphics card that is installed in a portin a motherboard of the device 104, or may be otherwise incorporatedwithin a peripheral device configured to interoperate with the device104.

According to an aspect, the processing unit 120 includes one or morearithmetic logic units (ALUs), for example, one or more graphics textureALUs that are configured to perform texture filtering to determinetexture colors for texture mapped pixels based on colors of nearbytexels (i.e., pixels of the texture). As will be described in detailbelow, the processing unit 120 can configured to map source locationdata to one or more ALUs for improved task performance.

It is further noted that if the techniques are implemented partially insoftware, the processing unit 120 may store instructions for thesoftware in a suitable, non-transitory computer-readable storage medium,e.g., internal memory 121, and may execute the instructions in hardwareusing one or more processors to perform the techniques of thisdisclosure. Any of the foregoing, including hardware, software, acombination of hardware and software, etc., may be considered to be oneor more processors.

The content encoder/decoder 122 may be any processing unit configured toperform content decoding. In some examples, the content encoder/decoder122 may be integrated into a motherboard of the device 104. The contentencoder/decoder 122 may include one or more processors, such as one ormore microprocessors, application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs), ALUs, digital signal processors(DSPs), video processors, discrete logic, software, hardware, firmware,other equivalent integrated or discrete logic circuitry, or anycombinations thereof. If the techniques are implemented partially insoftware, the content encoder/decoder 122 may store instructions for thesoftware in a suitable, non-transitory computer-readable storage medium,e.g., internal memory 123, and may execute the instructions in hardwareusing one or more processors to perform the techniques of thisdisclosure. Any of the foregoing, including hardware, software, acombination of hardware and software, etc., may be considered to be oneor more processors.

In some aspects, the content generation system 100 can include anoptional communication interface 126. The communication interface 126may include a receiver 128 and a transmitter 130. The receiver 128 maybe configured to perform any receiving function described herein withrespect to the device 104. The transmitter 130 may be configured toperform any transmitting function described herein with respect to thedevice 104. For example, the transmitter 130 may be configured totransmit information to another device, which may include a request forcontent. The receiver 128 and the transmitter 130 may be combined into atransceiver 132. In such examples, the transceiver 132 may be configuredto perform any receiving function and/or transmitting function describedherein with respect to the device 104.

Referring again to FIG. 1 , the graphics processing pipeline 107 caninclude an ALU/load engine 198 provided for mapping source location datato the GPU for improved task performance. In an aspect, the GPU includesone or more data processing elements, in in particular, one or moreshader units that typically allocates local storage to hold all sourcelocation data for a workload of an operation to be executed in thegraphics pipeline 107. According to an aspect, the ALU/load engine 198may configure the respective component (e.g., the shader unit) withinthe GPU to decode a single input fiber location to load the input data(e.g., workloads) to the local storage thereof for efficient executionof the subgroup.

In general, it is noted that a device, such as the device 104, may referto any device, apparatus, or system configured to perform one or moretechniques described herein. For example, a device may be a server, abase station, user equipment, a client device, a station, an accesspoint, a computer, e.g., a personal computer, a desktop computer, alaptop computer, a tablet computer, a computer workstation, or amainframe computer, an end product, an apparatus, a phone, a smartphone, a server, a video game platform or console, a handheld device,e.g., a portable video game device or a personal digital assistant(PDA), a wearable computing device, e.g., a smart watch, an augmentedreality device, or a virtual reality device, a non-wearable device, a display or di splay device, a television, a television set-top box, anintermediate network device, a digital media player, a video streamingdevice, a content streaming device, an in-car computer, any mobiledevice, any device configured to generate graphical content, or anydevice configured to perform one or more techniques described herein.

FIG. 2 is a block diagram 200 that illustrates an example graphicsprocessing unit (GPU 205) in accordance with one or more techniques ofthis disclosure. In general, the GPU 205 can correspond to an example ofthe processing unit 120 (or a component coupled thereto) as describedabove with respect to FIG. 1 . In an aspect, the processing unit 120 canbe GPU 205, which is a specialized electronic circuit designed torapidly manipulate and alter memory to accelerate the creation of imagesin a frame buffer intended for output to a display device, such asdisplay 131.

FIG. 2 shows an aspect in which the processing unit 120 comprises GPU205 to perform the respective image processing operations. Specifically,GPU 205 may be configured to perform graphics operations to render oneor more graphics to display 131, as described above. In a typicaloperation, when one of the software applications executing on the device104 requires graphics processing, the processing unit 120 may providegraphics commands and graphics data to GPU 205 for rendering to display131. The graphics data may include texture information, drawingcommands, and the like. As also described above, GPU 205 is built with ahighly-parallel structure that provides more efficient processing ofcomplex graphic related operations.

As shown, GPU 205 may include a plurality of processing elements, suchas one or more shader units (i.e., shader unit 210), that are configuredto operate on multiple vertices or pixels in a parallel manner.Moreover, GPU 205 may include internal memory 240 (e.g., correspondingto internal memory 121 of FIG. 1 ), such that GPU 205 may directly readdata from and write data to internal memory 240 without using a bus. Inother words, GPU 205 may process data locally using a local storage,instead of off-chip memory. This configuration will enable GPU 205 tooperate in a more efficient manner by eliminating the need of GPU 205 toread and write data via a bus, which may experience heavy bus traffic.In some instances, however, GPU 205 may not include a separate memory,but instead utilize system memory 124 via a bus.

In an aspect, internal memory 240 may include one or more volatile ornon-volatile memories or storage devices, such as, e.g., random accessmemory (RAM), static RAM (SRAM), dynamic RAM (DRAM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), Flash memory, a magnetic data media or an optical storagemedia. In addition, internal memory 240 may be coupled to cache 260 ofshader unit 210, the details of which will be described below.

As further shown in FIG. 2 , GPU 205 includes the one or more shaderunits 210, graphics processing pipeline 107, and texture pipeline 230.Moreover, one or more shader programs (e.g., “fibers” or “threads”) maybe executed by shader units 210 in GPU 205. Shader units 210 may alsoinclude one or more shader processors 220, each of which may include oneor more components for fetching and decoding operations, one or morearithmetic logic units (ALUs) 250 for carrying out arithmeticcalculations, one or more local caches 260, which can be other types oflocal memory and/or registers in various instances.

In a typical operation, GPU 205 may designate the one or more shaderunits 210 to perform a variety of shading operations such as vertexshading, hull shading, domain shading, geometry shading, pixel shading,and the like by sending commands to shader units 210 to execute one ormore of a vertex shader stage, a hull shader stage, a domain shaderstage, a geometry shader stage, and a pixel shader stage in graphicsprocessing pipeline 107. However, to execute these operations, theshader unit 210 in the GPU 210 will first allocate its local storage(e.g., cache 260) to hold all source location data for the workload,before then loading the input data to execute the operation.

In an aspect and referring back to FIG. 1 , the processing unit 120 caninclude ALU/load engine 198, which can be a control element executed byshader processor 220 in one aspect. Alternatively, shader processor 220can be a controller external to shader unit 210 and/or external to GPU205. In either case, ALU/load engine 198 is provided for mapping sourcelocation data to the GPU 205, and, more particularly, to mapping thesource location data to the local memory of the shader unit 210, such ascache 260, for example. In an aspect, the ALU/load engine 198 mayconfigure the shader unit 210 to decode a single fiber input source forthen loading the input data (e.g., workloads) to the local storage(e.g., cache 260) thereof for efficient execution of the respectivesubgroup.

FIG. 3 is a block diagram 300 that illustrates an example shader unit inaccordance with one or more techniques of this disclosure. In thisaspect, the ALU/load engine 198 is executed by shader processor 220 andconfigured to control operation of the one or more ALUs 250 as well asthe reading and writing of data to and from the local memory of theshader unit 210 (e.g., cache 260). As further shown, cache 260 caninclude three sections of local memory storage 262, 264 and 266, whichis directly coupled to the one or more ALUs 250 as elements of shaderprocessor 220.

As described above, the GPU 205 may be configured to run parallel tasks,with each concurrently running task being considered a subgroup of theoperation executed by the GPU 205. The process for the invocation ofeach subgroup is conceptually shown in blocks 272, 274 and 276. That is,input location 272 is first loaded in first local storage 262, which inturn configures shader processor 220 to load input data 274 to secondlocal storage 264, before the operation is executed by the one or moreALUs 250 to generate output data 276 that is stored in third localstorage 266.

In an aspect, the input location data 272 may be source location mappingdata that maps the source location of the workload (i.e., the input data274) to the second local storage 264. For example, the input data 274can be image content that is stored in memory external to the GPU 205,such as image data stored in internal memory 124, for example. Inanother aspect, the input data 274 can be image content that is storedin memory the internal memory 121 that is external to the shader unit210, for example.

In either case, shader processor 220 typically allocates subgroup localstorage (e.g., local storage 262) to hold all the fiber source locationsfor the input data. In other words, for each data value of the workload,the shader processor 220 would load a corresponding fiber sourcelocation for that data value to the local storage. According to anexemplary aspect, the ALU/load engine 198 is configured with predefineddecoded rules that identify the workload's source location andrelationships between the invocations in a subgroup of the operationbased on a single fiber input location for that subgroup. As a result,both the source location information can be significantly reduced orremoved altogether.

According to an aspect, the single fiber input location can beconsidered data location identifier that is loaded or otherwise storedin the local storage 262 (e.g., the first section of the cache 260). TheALU/load engine 198 may be configured with predefined decoded rules todecode the source location of the workload (i.e., input data 274) basedon this single fiber input. In other words, using the predefineddecoding rules, the ALU/load engine 198 may be configured to access theinput data (e.g., image content, pixel data or the like) from externalmemory and load the input data to local storage 264 according to aspecific pattern for the memory as defined by the decoding rules. Inother words, the decoding rules define the location for the input datain the local storage 264 and the relationships between the invocationsfor the subgroups of the operation. Thus, during invocation, the one ormore ALUs 250 can access the input data from local storage 264, executethe designate operation, and generate output data 276 that is stored inlocal storage 266. By using a single fiber source input to map thesource location data for the ALUs, the consumption of local storage(e.g., local storage 262) is significantly reduced since only a signalinput value is loaded therein rather than all fiber source locations.

FIG. 4 illustrates an example diagram in accordance with one or moretechniques of this disclosure. As shown, a comparative example is shownfor source mapping between a typical system and an aspect of thetechniques described herein. In this aspect, the operation or functionperformed by the GPU 205 can be a matrix multiple application. Inparticular instances, the matrix multiply operation can be for imageanalysis or deep learning operations, such as to indicate at least oneof an identification of content of image data, a prediction of a nextimage in an image sequence of the content including the image data, andan identification of a missing part of the image data.

As shown, the matrix multiple operation can be divided into fourparallel tasks, i.e., subgroups 0-3 (subgroups 410A-410D), but it isnoted that the operation can be divided into more or lesstasks/subgroups in other aspects. As described above, shader logic unitsin a GPU will typically perform such operations by allocating its localstorage to hold all fiber source location data. For example, a pluralityof subgroups 410A to 410D is shown as subgroup collection 405A. In thiscase, subgroup0 is a matrix (n, m) of values and the shader unit wouldallocate the entire area of the cross-hatching of subgroup0 for theinput source data. That is, source data for all values in the matrix (n,m) would need to be allocated to the local memory.

However, according to an aspect of the techniques described herein, onlya single fiber source input or data location identifier is required forsubgroup0. Thus, the collection 405B shows the same subgroups 0-3, butuses a single fiber source input, i.e., data source control parametervalue 420. In this aspect, only the upper left coordinate of each matrixneeds to be passed to the local storage (e.g., storage 262) of theshader processor 220, instead of every fiber source location as shownfor subgroup0 of collection 405A. As a result, the reduction ratio ofconsumer memory of the local storage is effective 1 to n*m values. It isnoted that while the upper left coordinate of the matrix (n, m) isprovided as the control parameter value 420, a different singlecoordinate value can be used as the control parameter value 420 in otheraspects. Moreover, a control parameter value is provided for each of thesubgroups 410A-410D as further shown, which is again the upper leftcoordinate of each matrix for each subgroup.

Referring back to FIG. 3 , the control parameter value 420 for eachsubgroup is loaded as the input location data value 272 to local storage262 of the shader processor 220. As also described above, the ALU/loadengine 198 is configured to decode this control parameter value 420based on predefined decoding rules, which is single fiber input or datalocation identifier, to load the input data for the workload of theoperation. In an aspect, the input data 274 can be image data that canaccessed from internal memory 240 or alternatively from system memory124. In either case, the ALU/load engine 198 is configured withpredetermined rules that enable it to identify the memory addresses ofthe input data, such as matrix (n, m), from the external memory.Moreover, the ALU/load engine 198 is also configured to load this inputdata 274 in a fixed or set pattern to local storage 264, which can thenbe directly accessed by the one or more ALUs 250 when the subgroups areinvoked for execution of the operation, which can be a deep learningoperation, image processing operation, or the like. The output datagenerated by the operation of the ALUs 250 is then stored in localstorage 266, where it can subsequently be accessed by other componentsof the image processing elements (e.g., graphics processing pipeline107). According to these techniques, the required memory used in localstorage 262 is effectively reduced or compressed for each subgroup,which effectively frees up memory for additional processes and minimizesdata flow for each operation.

As described above, an aspect of techniques described herein configurethe one or more shader units 210 to perform an operation, such as a deeplearning operation like a matrix multiply operation. In this aspect, thecontrol parameter value 420 defines a mapping of source location inputdata for the workload of the operation. In another aspect, the controlparameter value 420 can also define relationships for the subgroupinvocations. For instance, the designated operation for the one or moreALUs 250 can be a FFT (fast Fourier transform) operation of the inputdata.

FIG. 5 illustrates an example diagram in accordance with one or moretechniques of this disclosure. In general, FFT operations aremulti-staged or multilayered with the data going from one stage to thenext, such the output data of one layer (e.g., a first source) of thecomputations become the input data (e.g., a first destination) of thelatter, and so forth. Moreover, the order and grouping of the data varyfrom one stage to the next so it is necessary to ensure that correctdata is accessed from memory for each computation in the FFT operation.

As shown in FIG. 5 , first and second shuffle or reordering modes (e.g.,modes 0 and 1) are shown for mapping the source data to the destinationdata at each stage. More particularly, the first stage of the FFToperation reorders the data values as shown from source data 510 todestination data 520. That is, assuming the row of data values (e.g.,the subgroup of n invocations) is from left to right (e.g., values atpositions 0-7 for n=8), shuffle mode 0 reorders the data to positions 1,0, 3, 2, 5, 4, 7, 6. Conventionally, the shader unit 210 would allocatethe invocation's location for each destination (i.e., the diagonal crosshatching of the destination values 520). Thus, referring back to FIG. 3, data values for each invocation's location would be stored in localstorage 262 as the input source location data 272. According to anaspect, the ALU/load engine 198 can be configured with predefined rulesfor each shuffle mode or reordering mode. As a result, rather thanneeding to allocate n values to the local storage 262, the ALU/loadengine 198 can allocate a single control parameter value 420 for eachlayer or stage of the FFT that defines the mode for the subgroup (e.g.,the layer or stage of the FFT operation).

FIG. 5 also shows a second shuffle mode (i.e., mode 1). In thisinstance, the destinations 520 from the first stage become the sources520 for the second stage of the FFT operation. Moreover, mode 1 shows areordering of the n source values from left to right (e.g., values atpositions 0-7 for n=8) to a reordering of destination data values 530 topositions 0, 3, 1, 5, 2, 6, 4, 7. It should be appreciated that thespecific reordering of mode 0 and mode 1 are only for illustrativepurposes. Again, in this aspect, the single control parameter value 420for this stage will be indicative of mode 1 and the ALU/load engine 198can be configured with predetermined rules that determine the mapping todestinations so that each invocation's location is not required to beloaded to local storage as described above.

In another aspect, the single control parameter value 420 can furtherdefine the relationships of the invocations of the subgroup. Thus, in anaspect, the predefined rules can further configure ALU/load engine 198to determine the order or sequence of the shuffle modes for each stageof the FFT operation. For example, if the single control parameter value420 identifies “mode 0”, the ALU/load engine 198 may be configured,according to the predefined rules, to determine the that next shufflemode is “mode 1” and so forth. It is further noted that the predefinedrules can be generated by a designer of the GPU 205 according to anaspect.

FIG. 6 illustrates an example flowchart 600 of an example method inaccordance with one or more techniques of this disclosure. The methodmay be performed by an apparatus, such as a GPU 205, as described above.In an aspects, the steps highlighted by a dashed box illustrate optionalsteps in an aspect of the technique described herein.

At block 602, the ALU/load engine 198 can be configured with one or aplurality of predefined rules that enables the ALU/load engine 198 toderive each fiber input location for each subgroup by a single fibersource input (i.e., a single control parameter value 420). Thepredefined rules can further define the relationships of the invocationsof the subgroup.

Next, at block 604, the ALU/load engine 198 can be configured to loadthe single control parameter value 420 to local storage. For example, ifthe predefined operation is a matrix multiple operation, the singlecontrol parameter value 420 for each subgroup can be the upper leftcoordinate of each matrix (n, m) corresponding to each subgroup. In aparticular instance, the loading of each single control parameter value420 for each subgroup can be executed as part of a process(es) in agraphics processing pipeline. At block 606, the ALU/load engine 198 canbe configured to decode each single control parameter value 420according to the one or more predefined rules. In an aspect, thedecoding of the single control parameter value 420 maps the sourcelocation of the input data to the local storage of the GPU 205, andparticularly to the shader unit 210 according to an aspect.

At block 608, the ALU/load engine 198 can be configured to load theinput data (e.g., the workload of the specified operation) to localstorage of the shader unit 210 in an aspect. The specific data values ofthe input data can be loaded in the local storage according to a pattern(e.g., pattern of memory addresses) that are decoded from the singlecontrol parameter value 420. Using this data, the ALU/load engine 198 isfurther configured to control the ALUs (e.g., ALUS 250) to invoke theparticular subgroup for the desired operation at block 610. As notedabove, the predefined rules can further define the relationship (e.g.,the order) of invocations of subgroups for the task.

At block 612, the output data generated by the ALUs is stored in localstorage. Finally, at block 614, the programmable shader unit 210 cananalyze the output results of the performed by the ALUs to determinefeatures of the data workload. As a result, the ALUs 250 can be utilizedby the GPU 205 for performing deep learning and image processingoperations and maximizing use of resources during image processing.

Effectively, the subject matter described herein can be implemented torealize one or more benefits or advantages. For instance, the techniquesdisclosed herein enable the GPU to reduce the local storage required toinvoke each subgroup of an operation, which increases the overallperformance of the shader unit(s), which can then issue moresubgroups/tasks. As a result the apparatus and method further reduce thepower consumption of the GPU 205 since it is loading less data (i.e.,the source input data) into the local storage of the shader unit(s).This result in turn improves the overall performance of the GPU sincethe latency of waiting for ready data is also reduce. Thus, theprocessing techniques herein can improve or speed up data processing orexecution and also improve resource or data utilization and/or resourceefficiency.

In accordance with this disclosure, the term “or” may be interpreted as“and/or” where context does not dictate otherwise. Additionally, whilephrases such as “one or more” or “at least one” or the like may havebeen used for some features disclosed herein but not others, thefeatures for which such language was not used may be interpreted to havesuch a meaning implied where context does not dictate otherwise.

In one or more examples, the functions described herein may beimplemented in hardware, software, firmware, or any combination thereof.For example, although the term “processing unit” has been usedthroughout this disclosure, such processing units may be implemented inhardware, software, firmware, or any combination thereof. If anyfunction, processing unit, technique described herein, or other moduleis implemented in software, the function, processing unit, techniquedescribed herein, or other module may be stored on or transmitted overas one or more instructions or code on a computer-readable medium.Computer-readable media may include computer data storage media orcommunication media including any medium that facilitates transfer of acomputer program from one place to another. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media, which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. By way of example, and not limitation, suchcomputer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices. Disk and disc, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media. Acomputer program product may include a computer-readable medium.

The code may be executed by one or more processors, such as one or moredigital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), arithmetic logic units(ALUs), field programmable logic arrays (FPGAs), or other equivalentintegrated or discrete logic circuitry. Accordingly, the term“processor,” as used herein may refer to any of the foregoing structureor any other structure suitable for implementation of the techniquesdescribed herein. Also, the techniques could be fully implemented in oneor more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs, e.g., a chip set. Various components,modules or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily need realization by differenthardware units. Rather, as described above, various units may becombined in any hardware unit or provided by a collection ofintraoperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method for mapping a source location of inputdata for processing by a graphics processing unit, the methodcomprising: configuring a processing element of the graphics processingunit with at least one predefined rule for decoding a data sourceparameter for a task to be executed by the graphics processing unit;storing the data source parameter in local storage of the processingelement; decoding, by the processing element, the data source parameteraccording to the at least one predefined rule to determine a sourcelocation of the input data and at least one relationship betweeninvocations of the task; loading, to the local storage of the processingelement, the input data from a plurality of memory addresses of thesource location that are determined by the decoded data sourceparameter; executing the task, by at least one logic unit of theprocessing element, on the loaded input data to generate output data;and configuring the at least one logic unit to generate the output databased on a matrix multiply operation, wherein the input data comprisesat least one input matrix and the data source parameter comprises asingle element value within the at least one input matrix.
 2. The methodof claim 1, further comprising configuring the at least one logic unitto decode the single element value to determine the source location ofthe plurality of memory addresses.
 3. The method of claim 2, whereineach data input value of the at least one matrix is stored in arespective memory address of the plurality of memory addresses.
 4. Themethod of claim 1, further comprising calculating, by the at least onelogic unit, the output data by multiplying the at least one input matrixby one or more additional input matrices to generate the output data. 5.The method of claim 1, further comprising storing the output data in thelocal storage of the processing element.
 6. The method of claim 1,further comprising defining a mode of operation for the at least onelogic unit to execute the task.
 7. The method of claim 6, wherein thetask comprises a fast Fourier transform operation of the loaded inputdata to generate the output data.
 8. The method of claim 7, furthercomprising configuring the at least one logic unit to determine shuffleinstructions for the fast Fourier transform operation based on thedefined mode of operation.
 9. The method of claim 1, wherein the taskexecuted by the at least one logic unit is a deep learning operation.10. The method of claim 9, wherein the input data is image data and theoutput data for the executed deep learning operation indicates at leastone of an identification of content of the image data, a prediction of anext image in an image sequence of the content including the image data,and an identification of a missing part of the image data.
 11. Themethod of claim 1, further comprising loading the input data to thelocal storage of the processing element from the electronic memory thatis located external to the graphics processing unit.
 12. The method ofclaim 1, wherein the processing element comprises a shader unit of thegraphics processing unit.
 13. An apparatus for mapping a source locationof input data for processing by a graphics processing unit, theapparatus comprising: a memory; and at least one processor coupled tothe memory and configured to: configure a processing element of thegraphics processing unit with at least one predefined rule for decodinga data source parameter for a task to be executed by the graphicsprocessing unit; store the data source parameter in local storage of theprocessing element; configure the processing element to decode the datasource parameter according to the at least one predefined rule todetermine a source location of the input data and at least onerelationship between invocations of the task; load, to the local storageof the processing element, the input data from a plurality of memoryaddresses of the source location that are determined by the decoded datasource parameter; configure at least one logic unit of the processingelement to execute the task on the loaded input data to generate outputdata; and configure the at least one logic unit to generate the outputdata based on a matrix multiply operation, wherein the input datacomprises at least one input matrix and the data source parametercomprises a single element value within the at least one input matrix.14. The apparatus of claim 13, wherein the at least one processor isfurther configured to configure the at least one logic unit to decodethe single element value to determine the source location of theplurality of memory addresses.
 15. The apparatus of claim 14, whereineach data input value of the at least one matrix is stored in arespective memory address of the plurality of memory addresses.
 16. Theapparatus of claim 13, wherein the at least one processor is furtherconfigured to control the at least one logic unit to calculate theoutput data by multiplying the at least one input matrix by one or moreadditional input matrices to generate the output data.
 17. The apparatusof claim 13, wherein the at least one processor is further configured tostore the output data in the local storage of the processing element.18. The apparatus of claim 13, wherein the at least one processor isfurther configured to define a mode of operation for the at least onelogic unit to execute the task.
 19. The apparatus of claim 18, whereinthe task comprises a fast Fourier transform operation of the loadedinput data to generate the output data.
 20. The apparatus of claim 19,wherein the at least one processor is further configured to configurethe at least one logic unit to determine shuffle instructions for thefast Fourier transform operation based on the defined mode of operation.21. The apparatus of claim 13, wherein the operation executed by the atleast one logic unit is a deep learning operation.
 22. The apparatus ofclaim 21, wherein the input data is image data and the output data forthe executed deep learning operation indicates at least one of anidentification of content of the image data, a prediction of a nextimage in an image sequence of the content including the image data, andan identification of a missing part of the image data.
 23. The apparatusof claim 13, wherein the at least one processor is further configured toload the input data to the local storage of the processing element fromthe electronic memory that is located external to the graphicsprocessing unit.
 24. The apparatus of claim 13, wherein the processingelement comprises a shader unit of the graphics processing unit.
 25. Anapparatus for mapping a source location of input data for processing bya graphics processing unit, the apparatus comprising: means forconfiguring a processing element of the graphics processing unit with atleast one predefined rule for decoding a data source parameter for atask to be executed by the graphics processing unit; means for storingthe data source parameter in local storage of the processing element;means for configuring the processing element to decode the data sourceparameter according to the at least one predefined rule to determine asource location of the input data and at least one relationship betweeninvocations of the task; means for loading, to the local storage of theprocessing element, the input data from a plurality of memory addressesof the source location that are determined by the decoded data sourceparameter; means for controlling at least one logic unit of theprocessing element to execute the task on the loaded input data togenerate output data; and means for configuring the at least one logicunit to generate the output data based on a matrix multiply operation,wherein the input data comprises at least one input matrix and the datasource parameter comprises a single element value within the at leastone input matrix.